Deep generative models, and particularly the family of variational auto-encoders (vaes) [1]}, [2]}, address unsupervised representation learning from a probabilistic viewpoint by approximating the posterior over the representation space. Despite their widespread adoption, model parameters are still deterministic and sensible to ill-suited local minima, e.g. over-fitted to training data [3]}, which may cause poor generalization. The Bayesian nns favour inference over the nn weights for addressing such issues [4]}, [5]}. This approach deduce distributions on data space by learning posterior distributions on the parameter space. However, several shortcomings [6]}, e.g. expensive training, tuning and implementations, often limit their applicability to autoencoder-style models. Alternatively, other methods such as deep ensembles [7]}, stochastic weight averaging (swag) [8]} or Monte-Carlo dropout [9]} also promise Bayesian approximations to nn weight's posterior, but at the cost of increased training time, poor empirical performance or limited Bayesian interpretation.
